Huaijun Zhou: Department of Animal Science, University of California, Davis, California, USA; email: hzhou@ucdavis.edu, dguan@ucdavis.edu, qtlcheng@ucdavis.edu, ucywang@ucdavis.edu.
Emily Clark: The Roslin Institute, University of Edinburgh, Edinburgh, Midlothian, United Kingdom; email: Emily.Clark@roslin.ed.ac.uk.
Dailu Guan: Department of Animal Science, University of California, Davis, California, USA; email: hzhou@ucdavis.edu, dguan@ucdavis.edu, qtlcheng@ucdavis.edu, ucywang@ucdavis.edu.
Lingzhao Fang: Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark; email: lingzhao.fang@qgg.au.dk.
Hao Cheng: Department of Animal Science, University of California, Davis, California, USA; email: hzhou@ucdavis.edu, dguan@ucdavis.edu, qtlcheng@ucdavis.edu, ucywang@ucdavis.edu.
Christopher K Tuggle: Department of Animal Science, Iowa State University, Ames, Iowa, USA; email: cktuggle@iastate.edu, muskan@iastate.edu.
Muskan Kapoor: Department of Animal Science, Iowa State University, Ames, Iowa, USA; email: cktuggle@iastate.edu, muskan@iastate.edu.
Ying Wang: Department of Animal Science, University of California, Davis, California, USA; email: hzhou@ucdavis.edu, dguan@ucdavis.edu, qtlcheng@ucdavis.edu, ucywang@ucdavis.edu.
Transcriptional regulation in response to diverse physiological cues involves complicated biological processes. Recent initiatives that leverage whole genome sequencing and annotation of regulatory elements significantly contribute to our understanding of transcriptional gene regulation. Advances in the data sets available for comparative genomics and epigenomics can identify evolutionarily constrained regulatory variants and shed light on noncoding elements that influence transcription in different tissues and developmental stages across species. Most epigenomic data, however, are generated from healthy subjects at specific developmental stages. To bridge the genotype-phenotype gap, future research should focus on generating multidimensional epigenomic data under diverse physiological conditions. Farm animal species offer advantages in terms of feasibility, cost, and experimental design for such integrative analyses in comparison to humans. Deep learning modeling and cutting-edge technologies in sequencing and functional screening and validation also provide great promise for better understanding transcriptional regulation in this dynamic field.
